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El. knyga: Analyzing Spatial Models of Choice and Judgment with R

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, (University of Georgia), (University of California, Davis), , ,
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R is an interpreted language designed for statistical computation. Free, ubiquitous, and powerful, it is an excellent choice for such computations and is rapidly becoming a de facto standard in the field. This text concentrates on construction and analysis of spatial models using R, particularly in the fields of political choice data. Introductory chapters describe both the political context (the spatial theory of voting and data types analyzed by spatial voting models) and provide a foundation for programming in R (although this text does not replace a basic introduction to R programming). Further chapters address specific issues on the models, such as scales, similarities and dissimilarities data, rating and binary choice data, and several advanced topics in latent estimates and ordinal and dynamic IRT models. Examples of calculations using international data on politics and voting patterns, and many fully-worked programming examples round out the text. Annotation ©2014 Ringgold, Inc., Portland, OR (protoview.com)

Modern Methods for Evaluating Your Social Science Data

With recent advances in computing power and the widespread availability of political choice data, such as legislative roll call and public opinion survey data, the empirical estimation of spatial models has never been easier or more popular. Analyzing Spatial Models of Choice and Judgment with R demonstrates how to estimate and interpret spatial models using a variety of methods with the popular, open-source programming language R.

Requiring basic knowledge of R, the book enables researchers to apply the methods to their own data. Also suitable for expert methodologists, it presents the latest methods for modeling the distances between points—not the locations of the points themselves. This distinction has important implications for understanding scaling results, particularly how uncertainty spreads throughout the entire point configuration and how results are identified.

In each chapter, the authors explain the basic theory behind the spatial model, then illustrate the estimation techniques and explore their historical development, and finally discuss the advantages and limitations of the methods. They also demonstrate step by step how to implement each method using R with actual datasets. The R code and datasets are available on the book’s website.

Recenzijos

"The book is well organized. The R code in the book is well documented and the R outputs are clearly interpreted. The book is accessible to applied researchers who are more interested in applying the methods than in delving into their underlying theory. The step-by-step instructions given allow the reader to directly apply the methods. The understanding of the theoretical arguments, however, only requires college-level algebra." Journal of the American Statistical Association, Vol. 110, 2015



"For someone working outside of the fields of spatial modeling and political science, simple and informative plots of results are vital to understanding exactly what spatial modeling is capable of in political science. This book emphasizes this need too, and the graphics provided help to answer questions on various issues from different countries. The book provides a user-friendly chapter on R and throughout offers simple summaries of established functions, such as optimization methods, which are valuable for any R user regardless of their research focus and ability. On top of these are useful descriptions and examples of more advanced packages for spatial modeling, with printed R code and exercises for the reader. This book appears to be a great tool for established political scientists and spatial modelers, as well as those new to the fields who want to get up to speed." Significance, October 2014



"Analyzing Spatial Models of Choice and Judgment with R is the rare R-instructional book that succeeds on three levels. It clearly sets forth the psychological theory underlying its modeling method. It demonstrates how the mathematics used for the modeling provide principles of construction and interpretation consistent with that theory. And, it features very well-presented and sophisticated R codesophisticated enough to bring novice users of R very far along the path of proficiency and even enough, in some sections, to educate and challenge more advanced users. Students and practitioners interested in this field, or in latent space modeling in general, should consider it essential reading." Gary Evans, Journal of Statistical Software, June 2014

Preface xi
Author Biographies xix
1 Introduction 1(12)
1.1 The Spatial Theory of Voting
2(9)
1.1.1 Theoretical Development and Applications of the Spatial Voting Model
5(2)
1.1.2 The Development of Empirical Estimation Methods for Spatial Models of Voting
7(1)
1.1.3 The Basic Space Theory
8(3)
1.2 Summary of Data Types Analyzed by Spatial Voting Models
11(1)
1.3 Conclusion
11(2)
2 The Basics 13(26)
2.1 Data Basics in R
14(14)
2.1.1 Storage Modes
14(2)
2.1.2 Missing Values
16(2)
2.1.3 Recoding Data
18(1)
2.1.4 Probability Distributions and Random Numbers
19(1)
2.1.5 Loops and Functions
20(1)
2.1.6 The apply and sweep Functions
21(1)
2.1.7 Sorting Data
22(1)
2.1.8 Creating Scatter Plots and Kernel Density Plots
23(5)
2.2 Reading Data in R
28(7)
2.2.1 Reading Data from Stata into R
28(1)
2.2.2 Reading Data from SPSS into R
29(3)
2.2.3 Reading Text and Spreadsheet Files into R
32(3)
2.3 Writing Data in R
35(2)
2.3.1 Writing Data as a Stata File
35(1)
2.3.2 Writing Data as Text and .csv Files
36(1)
2.3.3 The dput/dget and save/load Functions in R
37(1)
2.4 Conclusion
37(2)
3 Analyzing Issue Scales 39(64)
3.1 Aldrich-McKelvey Scaling
40(26)
3.1.1 The basicspace Package in R
43(1)
3.1.2 Example 1: 2009 European Election Study (French Module)
44(5)
3.1.3 Example 2: 1968 American National Election Study Urban Unrest and Vietnam War Scales
49(6)
3.1.4 Estimating Bootstrapped Standard Errors for Aldrich-McKelvey Scaling
55(1)
3.1.5 Bayesian Aldrich-McKelvey Scaling
56(5)
3.1.6 Comparing Aldrich-McKelvey Standard Errors
61(5)
3.2 Basic Space Scaling: The blackbox Function
66(17)
3.2.1 Example 1: 2000 Convention Delegate Study
67(8)
3.2.2 Example 2: 2010 Swedish Parliamentary Candidate Survey
75(4)
3.2.3 Estimating Bootstrapped Standard Errors for Black Box Scaling
79(4)
3.3 Basic Space Scaling: The blackbox_transpose Function
83(8)
3.3.1 Example 1: 2000 and 2006 Comparative Study of Electoral Systems (Mexican Modules)
83(4)
3.3.2 Estimating Bootstrapped Standard Errors for Black Box Transpose Scaling
87(2)
3.3.3 Using the blackbox_transpose Function on Datasets with Large Numbers of Respondents
89(2)
3.4 Anchoring Vignettes
91(7)
3.5 Conclusion
98(1)
3.6 Exercises
99(4)
4 Analyzing Similarities and Dissimilarities Data 103(44)
4.1 Classical Metric Multidimensional Scaling
104(15)
4.1.1 Example 1: Nations Similarities Data
107(2)
4.1.2 Metric MDS Using Numerical Optimization
109(5)
4.1.3 Metric MDS Using Majorization (SMACOF)
114(1)
4.1.4 The smacof Package in R
114(5)
4.2 Non-metric Multidimensional Scaling
119(9)
4.2.1 Example 1: Nations Similarities Data
120(3)
4.2.2 Example 2: 90th US Senate Agreement Scores
123(5)
4.3 Bayesian Multidimensional Scaling
128(4)
4.3.1 Example 1: Nations Similarities Data
129(3)
4.4 Individual Differences Multidimensional Scaling
132(9)
4.4.1 Example 1: 2009 European Election Study (French Module)
137(4)
4.5 Conclusion
141(2)
4.6 Exercises
143(4)
5 Unfolding Analysis of Rating Scale Data 147(36)
5.1 Solving the Thermometers Problem
148(2)
5.2 Metric Unfolding Using the MLSMU6 Procedure
150(6)
5.2.1 Example 1: 1981 Interest Group Ratings of US Senators Data
154(2)
5.3 Metric Unfolding Using Majorization (SMACOF)
156(9)
5.3.1 Example 1: 2009 European Election Study (Danish Module)
159(4)
5.3.2 Comparing the MLSMU6 and SMACOF Metric Unfolding Procedures
163(2)
5.4 Bayesian Multidimensional Unfolding
165(13)
5.4.1 Example 1: 1968 American National Election Study Feeling Thermometers Data
166(12)
5.5 Conclusion
178(2)
5.6 Exercises
180(3)
6 Unfolding Analysis of Binary Choice Data 183(94)
6.1 The Geometry of Legislative Voting
184(2)
6.2 Reading Legislative Roll Call Data into R with the pscl Pack- age
186(3)
6.3 Parametric Methods - NOMINATE
189(25)
6.3.1 Obtaining Uncertainty Estimates with the Parametric Bootstrap
193(1)
6.3.2 Types of NOMINATE Scores
193(2)
6.3.3 Accessing DW-NOMINATE Scores
195(1)
6.3.4 The wnominate Package in R
196(1)
6.3.5 Example 1: The 108th US House
197(15)
6.3.6 Example 2: The First European Parliament (Using the Parametric Bootstrap)
212(2)
6.4 MQMC or α-NOMINATE
214(7)
6.4.1 The anominate Package in R
217(4)
6.5 Parametric Methods - Bayesian Item Response Theory
221(28)
6.5.1 The MCMCpack and pscl Packages in R
225(1)
6.5.2 Example 1: The 2000 Term of the US Supreme Court (Unidimensional IRT)
225(6)
6.5.3 Running Multiple Markov Chains in MCMCpack and pscl
231(3)
6.5.4 Example 2: The Confirmation Vote of Robert Bork to the US Supreme Court (Unidimensional IRT)
234(8)
6.5.5 Example 3: The 89th US Senate (Multidimensional IRT)
242(7)
6.6 Nonparametric Methods - Optimal Classification
249(15)
6.6.1 The oc Package in R
250(1)
6.6.2 Example 1: The French National Assembly during the Fourth Republic
250(8)
6.6.3 Example 2: 2008 American National Election Study Feeling Thermometers Data
258(6)
6.7 Conclusion: Comparing Methods for the Analysis of Legislative Roll Call Data
264(9)
6.7.1 Identification of the Model Parameters
267(2)
6.7.2 Comparing Ideal Point Estimates for the 111th US Senate
269(4)
6.8 Exercises
273(4)
7 Advanced Topics 277(34)
7.1 Using Latent Estimates as Variables
278(17)
7.1.1 Latent Variables as Independent Variables
278(4)
7.1.2 Latent Variables as Dependent Variables
282(4)
7.1.3 MIMIC Models
286(9)
7.2 Ordinal and Dynamic IRT Models
295(14)
7.2.1 IRT with Ordinal Choice Data
296(7)
7.2.2 Dynamic IRT
303(6)
7.3 Concluding Thoughts
309(2)
References 311(19)
Index 330
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